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2.
In Vivo ; 36(6): 2986-2992, 2022.
Article in English | MEDLINE | ID: covidwho-2100684

ABSTRACT

BACKGROUND/AIM: To report long-term survival results after trimodal approach for locally advanced rectal cancer (LARC) in the Covid-19 era. We herein illustrate a clinical application of Covid-death mean-imputation (CoDMI) algorithm in LARC patients with Covid-19 infection. PATIENTS AND METHODS: We analyzed 94 patients treated for primary LARC. Overall survival was calculated in months from diagnosis to first event (last follow-up/death). Because Covid-19 death events potentially bias survival estimation, to eliminate skewed data due to Covid-19 death events, the observed lifetime of Covid-19 cases was replaced by its corresponding expected lifetime in absence of the Covid-19 event using the CoDMI algorithm. Patients who died of Covid-19 (DoC) are mean-imputed by the Kaplan-Meier estimator. Under this approach, the observed lifetime of each DoC patient is considered as an "incomplete data" and is extended by an additional expected lifetime computed using the classical Kaplan-Meier model. RESULTS: Sixteen patients were dead of disease (DoD), 1 patient was DoC and 77 cases were censored (Cen). The DoC patient died of Covid-19 52 months after diagnosis. The CoDMI algorithm computed the expected future lifetime provided by the Kaplan-Meier estimator applied to the no-DoC observations as well as to the DoC data itself. Given the DoC event at 52 months, the CoDMI algorithm estimated that this patient would have died after 79.5 months of follow-up. CONCLUSION: The CoDMI algorithm leads to "unbiased" probability of overall survival in LARC patients with Covid-19 infection, compared to that provided by a naïve application of Kaplan-Meier approach. This allows for a proper interpretation/use of Covid-19 events in survival analysis. A user-friendly version of CoDMI is freely available at https://github.com/alef-innovation/codmi.


Subject(s)
COVID-19 , Radiation Oncology , Humans , Kaplan-Meier Estimate , COVID-19/epidemiology , Survival Analysis , Algorithms
3.
J Int Med Res ; 50(8): 3000605221119366, 2022 Aug.
Article in English | MEDLINE | ID: covidwho-2020829

ABSTRACT

OBJECTIVE: This study aimed to assess the time to severe coronavirus disease 2019 (COVID-19) and risk factors among confirmed COVID-19 cases in Southern Ethiopia. METHOD: This two-center retrospective cohort study involved patients with confirmed COVID-19 from 1 October 2020 to 30 September 2021. Kaplan-Meier graphs and log-rank tests were used to determine the pattern of COVID-19 severity among categories of variables. Bivariable and multivariable Cox proportional regression models were used to identify the risk factors of severe COVID-19. RESULTS: Four hundred thirteen patients with COVID-19 with a mean age of 41.9 ± 15.3 years were involved in the study. There were 194 severe cases (46.9.1%), including 77 (39.6%) deaths. The median time from symptom onset to severe COVID-19 was 8 days (interquartile range: 7-12 days). The risk factors for severe COVID-19 were age >65 (adjusted hazard ratio [AHR] = 2.65, 95% confidence interval [95%CI]: 1.02, 3.72), cough (AHR = 1.59, 95%CI: 1.39, 2.84), chest pain (AHR = 1.47, 95%CI: 1.34, 2.66), headache (AHR = 2.04, 95%CI: 1.43, 2.88), comorbidity (AHR = 1.3, 95%CI: 1.01, 2.04), asthma (AHR = 1.6. 95%CI: 1.04, 2.24), and symptom onset to admission more than 5 days (AHR = 0.48, 95%CI: 0.34, 0.68). CONCLUSION: Patients with symptoms and comorbidities should be closely monitored.


Subject(s)
COVID-19 , Adult , Ethiopia , Humans , Middle Aged , Proportional Hazards Models , Retrospective Studies , Risk Factors , Survival Analysis
4.
N Engl J Med ; 386(26): 2482-2494, 2022 06 30.
Article in English | MEDLINE | ID: covidwho-1984509

ABSTRACT

BACKGROUND: Ibrutinib, a Bruton's tyrosine kinase inhibitor, may have clinical benefit when administered in combination with bendamustine and rituximab and followed by rituximab maintenance therapy in older patients with untreated mantle-cell lymphoma. METHODS: We randomly assigned patients 65 years of age or older to receive ibrutinib (560 mg, administered orally once daily until disease progression or unacceptable toxic effects) or placebo, plus six cycles of bendamustine (90 mg per square meter of body-surface area) and rituximab (375 mg per square meter). Patients with an objective response (complete or partial response) received rituximab maintenance therapy, administered every 8 weeks for up to 12 additional doses. The primary end point was progression-free survival as assessed by the investigators. Overall survival and safety were also assessed. RESULTS: Among 523 patients, 261 were randomly assigned to receive ibrutinib and 262 to receive placebo. At a median follow-up of 84.7 months, the median progression-free survival was 80.6 months in the ibrutinib group and 52.9 months in the placebo group (hazard ratio for disease progression or death, 0.75; 95% confidence interval, 0.59 to 0.96; P = 0.01). The percentage of patients with a complete response was 65.5% in the ibrutinib group and 57.6% in the placebo group (P = 0.06). Overall survival was similar in the two groups. The incidence of grade 3 or 4 adverse events during treatment was 81.5% in the ibrutinib group and 77.3% in the placebo group. CONCLUSIONS: Ibrutinib treatment in combination with standard chemoimmunotherapy significantly prolonged progression-free survival. The safety profile of the combined therapy was consistent with the known profiles of the individual drugs. (Funded by Janssen Research and Development and Pharmacyclics; SHINE ClinicalTrials.gov number, NCT01776840.).


Subject(s)
Antineoplastic Combined Chemotherapy Protocols , Lymphoma, Mantle-Cell , Adenine/administration & dosage , Adenine/analogs & derivatives , Aged , Antineoplastic Combined Chemotherapy Protocols/adverse effects , Antineoplastic Combined Chemotherapy Protocols/therapeutic use , Bendamustine Hydrochloride/administration & dosage , Bendamustine Hydrochloride/adverse effects , Disease Progression , Humans , Lymphoma, Mantle-Cell/drug therapy , Lymphoma, Mantle-Cell/mortality , Maintenance Chemotherapy , Piperidines/administration & dosage , Piperidines/adverse effects , Protein Kinase Inhibitors/administration & dosage , Protein Kinase Inhibitors/adverse effects , Pyrazoles/administration & dosage , Pyrazoles/adverse effects , Pyrimidines/administration & dosage , Pyrimidines/adverse effects , Remission Induction , Rituximab/administration & dosage , Rituximab/adverse effects , Survival Analysis
5.
Stat Methods Med Res ; 31(11): 2164-2188, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-1968494

ABSTRACT

Cure models are a class of time-to-event models where a proportion of individuals will never experience the event of interest. The lifetimes of these so-called cured individuals are always censored. It is usually assumed that one never knows which censored observation is cured and which is uncured, so the cure status is unknown for censored times. In this paper, we develop a method to estimate the probability of cure in the mixture cure model when some censored individuals are known to be cured. A cure probability estimator that incorporates the cure status information is introduced. This estimator is shown to be strongly consistent and asymptotically normally distributed. Two alternative estimators are also presented. The first one considers a competing risks approach with two types of competing events, the event of interest and the cure. The second alternative estimator is based on the fact that the probability of cure can be written as the conditional mean of the cure status. Hence, nonparametric regression methods can be applied to estimate this conditional mean. However, the cure status remains unknown for some censored individuals. Consequently, the application of regression methods in this context requires handling missing data in the response variable (cure status). Simulations are performed to evaluate the finite sample performance of the estimators, and we apply them to the analysis of two datasets related to survival of breast cancer patients and length of hospital stay of COVID-19 patients requiring intensive care.


Subject(s)
COVID-19 , Models, Statistical , Humans , Survival Analysis , Probability , Regression Analysis , Computer Simulation
6.
Crit Care ; 26(1): 190, 2022 06 28.
Article in English | MEDLINE | ID: covidwho-1910342

ABSTRACT

BACKGROUND: Severe COVID-19 induced acute respiratory distress syndrome (ARDS) often requires extracorporeal membrane oxygenation (ECMO). Recent German health insurance data revealed low ICU survival rates. Patient characteristics and experience of the ECMO center may determine intensive care unit (ICU) survival. The current study aimed to identify factors affecting ICU survival of COVID-19 ECMO patients. METHODS: 673 COVID-19 ARDS ECMO patients treated in 26 centers between January 1st 2020 and March 22nd 2021 were included. Data on clinical characteristics, adjunct therapies, complications, and outcome were documented. Block wise logistic regression analysis was applied to identify variables associated with ICU-survival. RESULTS: Most patients were between 50 and 70 years of age. PaO2/FiO2 ratio prior to ECMO was 72 mmHg (IQR: 58-99). ICU survival was 31.4%. Survival was significantly lower during the 2nd wave of the COVID-19 pandemic. A subgroup of 284 (42%) patients fulfilling modified EOLIA criteria had a higher survival (38%) (p = 0.0014, OR 0.64 (CI 0.41-0.99)). Survival differed between low, intermediate, and high-volume centers with 20%, 30%, and 38%, respectively (p = 0.0024). Treatment in high volume centers resulted in an odds ratio of 0.55 (CI 0.28-1.02) compared to low volume centers. Additional factors associated with survival were younger age, shorter time between intubation and ECMO initiation, BMI > 35 (compared to < 25), absence of renal replacement therapy or major bleeding/thromboembolic events. CONCLUSIONS: Structural and patient-related factors, including age, comorbidities and ECMO case volume, determined the survival of COVID-19 ECMO. These factors combined with a more liberal ECMO indication during the 2nd wave may explain the reasonably overall low survival rate. Careful selection of patients and treatment in high volume ECMO centers was associated with higher odds of ICU survival. TRIAL REGISTRATION: Registered in the German Clinical Trials Register (study ID: DRKS00022964, retrospectively registered, September 7th 2020, https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00022964 .


Subject(s)
COVID-19 , Extracorporeal Membrane Oxygenation , Respiratory Distress Syndrome , COVID-19/therapy , Humans , Intensive Care Units , Pandemics , Respiratory Distress Syndrome/therapy , Survival Analysis
7.
Ann Saudi Med ; 42(3): 165-173, 2022.
Article in English | MEDLINE | ID: covidwho-1879590

ABSTRACT

BACKGROUND: About 5-10% of coronavirus disease 2019 (COVID-19) infected patients require critical care hospitalization and a variety of respiratory support, including invasive mechanical ventilation. Several nationwide studies from Saudi Arabia have identified common comorbidities but none were focused on mechanically ventilated patients in the Al-Ahsa region of Saudi Arabia. OBJECTIVES: Identify characteristics and risk factors for mortality in mechanically ventilated COVID-19 patients. DESIGN: Retrospective chart review SETTING: Two general hospitals in the Al-Ahsa region of Saudi Arabia PATIENTS AND METHODS: We included mechanically ventilated COVID-19 patients (>18 years old) admitted between 1 May and 30 November 2020, in two major general hospitals in the Al-Ahsa region, Saudi Arabia. Descriptive statistics were used to characterize patients. A multivariable Cox proportional hazards (CPH) model was used exploratively to identify hazard ratios (HR) of predictors of mortality. MAIN OUTCOME MEASURES: Patient characteristics, mortality rate, extubation rate, the need for re-intubation and clinical complications during hospitalization. SAMPLE SIZE AND CHARACTERISTICS: 154 mechanically ventilated COVID-19 patients with median (interquartile range) age of 60 (22) years; 65.6% male. RESULTS: Common comorbidities were diabetes (72.2%), hypertension (67%), cardiovascular disease (14.9%) and chronic kidney disease (CKD) (14.3%). In the multivariable CPH model, age >60 years old (HR=1.83, 95% CI 1.2-2.7, P=.002), CKD (1.61, 95% CI 0.9-2.6, P=.062), insulin use (HR=0.65, 95% CI 0.35-.08, P<.001), and use of loop diuretics (HR=0.51, 95% CI 0.4, P=.037) were major predictors of mortality. CONCLUSION: Common diseases in mechanically ventilated COVID-19 patients from the Al-Ahsa region were diabetes, hypertension, other cardiovascular diseases, and CKD in this exploratory analysis. LIMITATIONS: Retrospective, weak CPH model performance. CONFLICTS OF INTEREST: None.


Subject(s)
COVID-19 , Diabetes Mellitus , Hypertension , Renal Insufficiency, Chronic , Adolescent , COVID-19/epidemiology , COVID-19/therapy , Female , Humans , Hypertension/epidemiology , Male , Middle Aged , Respiration, Artificial , Retrospective Studies , Saudi Arabia/epidemiology , Survival Analysis
8.
J R Soc Interface ; 19(191): 20220124, 2022 06.
Article in English | MEDLINE | ID: covidwho-1874074

ABSTRACT

We present a new method for analysing stochastic epidemic models under minimal assumptions. The method, dubbed dynamic survival analysis (DSA), is based on a simple yet powerful observation, namely that population-level mean-field trajectories described by a system of partial differential equations may also approximate individual-level times of infection and recovery. This idea gives rise to a certain non-Markovian agent-based model and provides an agent-level likelihood function for a random sample of infection and/or recovery times. Extensive numerical analyses on both synthetic and real epidemic data from foot-and-mouth disease in the UK (2001) and COVID-19 in India (2020) show good accuracy and confirm the method's versatility in likelihood-based parameter estimation. The accompanying software package gives prospective users a practical tool for modelling, analysing and interpreting epidemic data with the help of the DSA approach.


Subject(s)
COVID-19 , Epidemics , Animals , COVID-19/epidemiology , Likelihood Functions , Prospective Studies , Survival Analysis
9.
Front Public Health ; 10: 857084, 2022.
Article in English | MEDLINE | ID: covidwho-1855467

ABSTRACT

Introduction: All Coronavirus disease 2019 (COVID-19) positive cases in Samtse District, Bhutan were isolated in the isolation facilities managed by the government hospitals. This study aimed to identify the socio-demographic risk factors for developing COVID-19 symptoms amongst these patients. Methods and Materials: A secondary data of the COVID-19 positive cases from isolation facilities of Samtse District from 5 May to 7 September 2021 was used for this study. Survival analysis was carried out to estimate the cumulative probability of symptom onset time by each risk factor. Kaplan-Meier curves were used to estimate the probabilities for the onset of symptoms at different time points and a log-rank test was employed to assess the differences between covariates. Results: A total of 449 patients were included, of which 55.2% were males and 73.3% (328) were aged >18 years. The mean age was 42 years with a range of 3 months to 83 years. Forty-seven percent (213) reported at least one symptom. Common symptoms were fever (32.3%, 145), headache (31.6%, 142), and cough (30.1%, 135), respectively. Males were 64% less likely to be symptomatic than females [adjusted hazard ratio (aHR) = 0.36, 95% confidence interval (CI) 0.183-0.917]. Farmers (aHR = 3.17, 95% CI 1.119-8.953), and drivers and loaders (aHR = 3.18, 95% CI 1.029-9.834) were 3 times more likely to be symptomatic compared to housewives. Residents of Samtse sub-districts were 5 times more likely to be symptomatic than those living in other sub-districts (aHR = 5.16, 95% CI 2.362-11.254). Conclusion: The risk of developing COVID-19 symptoms was being fe male, farmers, drivers and loaders, and residents of the Samtse sub-district. These high-risk groups should be provided additional care when in isolation facilities.


Subject(s)
COVID-19 , Bhutan/epidemiology , COVID-19/epidemiology , Cough , Female , Humans , Infant , Male , Risk Factors , Survival Analysis
10.
Int J Infect Dis ; 118: 150-154, 2022 May.
Article in English | MEDLINE | ID: covidwho-1838855

ABSTRACT

BACKGROUND: At present, it is unclear whether the extent of reduced risk of severe disease seen with SARS-Cov-2 Omicron variant infection is caused by a decrease in variant virulence or by higher levels of population immunity. METHODS: RdRp target delay (RTD) in the Seegene AllplexTM 2019-nCoV PCR assay is a proxy marker for the Delta variant. The absence of this proxy marker in the transition period was used to identify suspected Omicron infections. Cox regression was performed for the outcome of hospital admission in those who tested positive for SARS-CoV-2 on the Seegene AllplexTM assay from November 1 to December 14, 2021 in the Western Cape Province, South Africa, in the public sector. Adjustments were made for vaccination status and prior diagnosis of infection. RESULTS: A total of 150 cases with RTD and 1486 cases without RTD were included. Cases without RTD had a lower hazard of admission (adjusted hazard ratio [aHR], 0.56; 95% confidence interval [CI], 0.34-0.91). Complete vaccination was protective against admission, with an aHR of 0.45 (95% CI, 0.26-0.77). CONCLUSION: Omicron has resulted in a lower risk of hospital admission compared with contemporaneous Delta infection, when using the proxy marker of RTD. Under-ascertainment of reinfections with an immune escape variant remains a challenge to accurately assessing variant virulence.


Subject(s)
COVID-19 , Hepatitis D , COVID-19/diagnosis , Humans , Polymerase Chain Reaction , RNA-Dependent RNA Polymerase , SARS-CoV-2/genetics , South Africa/epidemiology , Survival Analysis
11.
Contemp Clin Trials ; 119: 106758, 2022 08.
Article in English | MEDLINE | ID: covidwho-1773152

ABSTRACT

In clinical trials with the objective to evaluate the treatment effect on time to recovery, such as investigational trials on therapies for COVID-19 hospitalized patients, the patients may face a mortality risk that competes with the opportunity to recover (e.g., be discharged from the hospital). Therefore, an appropriate analytical strategy to account for death is particularly important due to its potential impact on the estimation of the treatment effect. To address this challenge, we conducted a thorough evaluation and comparison of nine survival analysis methods with different strategies to account for death, including standard survival analysis methods with different censoring strategies and competing risk analysis methods. We report results of a comprehensive simulation study that employed design parameters commonly seen in COVID-19 trials and case studies using reconstructed data from a published COVID-19 clinical trial. Our research results demonstrate that, when there is a moderate to large proportion of patients who died before observing their recovery, competing risk analyses and survival analyses with the strategy to censor death at the maximum follow-up timepoint would be able to better detect a treatment effect on recovery than the standard survival analysis that treat death as a non-informative censoring event. The aim of this research is to raise awareness of the importance of handling death appropriately in the time-to-recovery analysis when planning current and future COVID-19 treatment trials.


Subject(s)
COVID-19 , Death , COVID-19/drug therapy , Computer Simulation , Humans , Survival Analysis
12.
CMAJ ; 194(11): E408-E414, 2022 03 21.
Article in English | MEDLINE | ID: covidwho-1753218

ABSTRACT

BACKGROUND: With the declaration of the global pandemic, surgical slowdowns were instituted to conserve health care resources for anticipated surges in patients with COVID-19. The long-term implications on survival of these slowdowns for patients with cancer in Canada is unknown. METHODS: We constructed a microsimulation model based on real-world population data on cancer care from Ontario, Canada, from 2019 and 2020. Our model estimated wait times for cancer surgery over a 6-month period during the pandemic by simulating a slowdown in operating room capacity (60% operating room resources in month 1, 70% in month 2, 85% in months 3-6), as compared with simulated prepandemic conditions with 100% resources. We used incremental differences in simulated wait times to model survival using per-day hazard ratios for risk of death. Primary outcomes included life-years lost per patient and per cancer population. We conducted scenario analyses to evaluate alternative, hypothetical scenarios of different levels of surgical slowdowns on risk of death. RESULTS: The simulated model population comprised 22 799 patients waiting for cancer surgery before the pandemic and 20 177 patients during the pandemic. Mean wait time to surgery prepandemic was 25 days and during the pandemic was 32 days. Excess wait time led to 0.01-0.07 life-years lost per patient across cancer sites, translating to 843 (95% credible interval 646-950) life-years lost among patients with cancer in Ontario. INTERPRETATION: Pandemic-related slowdowns of cancer surgeries were projected to result in decreased long-term survival for many patients with cancer. Measures to preserve surgical resources and health care capacity for affected patients are critical to mitigate unintended consequences.


Subject(s)
COVID-19/epidemiology , Neoplasms/mortality , Neoplasms/surgery , Pandemics , Time-to-Treatment , Delayed Diagnosis , Humans , Neoplasms/diagnosis , Ontario/epidemiology , Risk Assessment , Survival Analysis , Uncertainty , Waiting Lists
13.
Front Immunol ; 13: 794006, 2022.
Article in English | MEDLINE | ID: covidwho-1742215

ABSTRACT

To rapidly prognosticate and generate hypotheses on pathogenesis, leukocyte multi-cellularity was evaluated in SARS-CoV-2 infected patients treated in India or the United States (152 individuals, 384 temporal observations). Within hospital (<90-day) death or discharge were retrospectively predicted based on the admission complete blood cell counts (CBC). Two methods were applied: (i) a "reductionist" one, which analyzes each cell type separately, and (ii) a "non-reductionist" method, which estimates multi-cellularity. The second approach uses a proprietary software package that detects distinct data patterns generated by complex and hypothetical indicators and reveals each data pattern's immunological content and associated outcome(s). In the Indian population, the analysis of isolated cell types did not separate survivors from non-survivors. In contrast, multi-cellular data patterns differentiated six groups of patients, including, in two groups, 95.5% of all survivors. Some data structures revealed one data point-wide line of observations, which informed at a personalized level and identified 97.8% of all non-survivors. Discovery was also fostered: some non-survivors were characterized by low monocyte/lymphocyte ratio levels. When both populations were analyzed with the non-reductionist method, they displayed results that suggested survivors and non-survivors differed immunologically as early as hospitalization day 1.


Subject(s)
Blood Cell Count/methods , COVID-19/immunology , SARS-CoV-2/physiology , Adult , COVID-19/diagnosis , COVID-19/mortality , Diagnostic Tests, Routine , Female , Humans , India , Male , Middle Aged , Precision Medicine , Retrospective Studies , Software , Survival Analysis , United States
14.
Sci Rep ; 12(1): 3721, 2022 03 08.
Article in English | MEDLINE | ID: covidwho-1735274

ABSTRACT

It is unclear if changes in public behaviours, developments in COVID-19 treatments, improved patient care, and directed policy initiatives have altered outcomes for minority ethnic groups in the second pandemic wave. This was a prospective analysis of patients aged ≥ 16 years having an emergency admission with SARS-CoV-2 infection between 01/09/2020 and 17/02/2021 to acute NHS hospitals in east London. Multivariable survival analysis was used to assess associations between ethnicity and mortality accounting for predefined risk factors. Age-standardised rates of hospital admission relative to the local population were compared between ethnic groups. Of 5533 patients, the ethnic distribution was White (n = 1805, 32.6%), Asian/Asian British (n = 1983, 35.8%), Black/Black British (n = 634, 11.4%), Mixed/Other (n = 433, 7.8%), and unknown (n = 678, 12.2%). Excluding 678 patients with missing data, 4855 were included in multivariable analysis. Relative to the White population, Asian and Black populations experienced 4.1 times (3.77-4.39) and 2.1 times (1.88-2.33) higher rates of age-standardised hospital admission. After adjustment for various patient risk factors including age, sex, and socioeconomic deprivation, Asian patients were at significantly higher risk of death within 30 days (HR 1.47 [1.24-1.73]). No association with increased risk of death in hospitalised patients was observed for Black or Mixed/Other ethnicity. Asian and Black ethnic groups continue to experience poor outcomes following COVID-19. Despite higher-than-expected rates of hospital admission, Black and Asian patients also experienced similar or greater risk of death in hospital since the start of the pandemic, implying a higher overall risk of COVID-19 associated death in these communities.


Subject(s)
COVID-19/mortality , Hospitalization/statistics & numerical data , Adult , Aged , COVID-19/ethnology , COVID-19/therapy , COVID-19/virology , Female , Hospitals , Humans , Intensive Care Units , London , Male , Middle Aged , Proportional Hazards Models , Risk Factors , SARS-CoV-2/isolation & purification , Survival Analysis
15.
BMC Anesthesiol ; 22(1): 59, 2022 03 04.
Article in English | MEDLINE | ID: covidwho-1724413

ABSTRACT

BACKGROUND: Data on the lung respiratory mechanics and gas exchange in the time course of COVID-19-associated respiratory failure is limited. This study aimed to explore respiratory mechanics and gas exchange, the lung recruitability and risk of overdistension during the time course of mechanical ventilation. METHODS: This was a prospective observational study in critically ill mechanically ventilated patients (n = 116) with COVID-19 admitted into Intensive Care Units of Sechenov University. The primary endpoints were: «optimum¼ positive end-expiratory pressure (PEEP) level balanced between the lowest driving pressure and the highest SpO2 and number of patients with recruitable lung on Days 1 and 7 of mechanical ventilation. We measured driving pressure at different levels of PEEP (14, 12, 10 and 8 cmH2O) with preset tidal volume, and with the increase of tidal volume by 100 ml and 200 ml at preset PEEP level, and calculated static respiratory system compliance (CRS), PaO2/FiO2, alveolar dead space and ventilatory ratio on Days 1, 3, 5, 7, 10, 14 and 21. RESULTS: The «optimum¼ PEEP levels on Day 1 were 11.0 (10.0-12.8) cmH2O and 10.0 (9.0-12.0) cmH2O on Day 7. Positive response to recruitment was observed on Day 1 in 27.6% and on Day 7 in 9.2% of patients. PEEP increase from 10 to 14 cmH2O and VT increase by 100 and 200 ml led to a significant decrease in CRS from Day 1 to Day 14 (p < 0.05). Ventilatory ratio was 2.2 (1.7-2,7) in non-survivors and in 1.9 (1.6-2.6) survivors on Day 1 and decreased on Day 7 in survivors only (p < 0.01). PaO2/FiO2 was 105.5 (76.2-141.7) mmHg in non-survivors on Day 1 and 136.6 (106.7-160.8) in survivors (p = 0.002). In survivors, PaO2/FiO2 rose on Day 3 (p = 0.008) and then between Days 7 and 10 (p = 0.046). CONCLUSION: Lung recruitability was low in COVID-19 and decreased during the course of the disease, but lung overdistension occurred at «intermediate¼ PEEP and VT levels. In survivors gas exchange improvements after Day 7 mismatched CRS. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04445961 . Registered 24 June 2020-Retrospectively registered.


Subject(s)
COVID-19/epidemiology , COVID-19/therapy , Lung/physiopathology , Respiration, Artificial/statistics & numerical data , Respiratory Insufficiency/epidemiology , Aged , COVID-19/physiopathology , Critical Care/methods , Female , Humans , Male , Middle Aged , Noninvasive Ventilation/statistics & numerical data , Positive-Pressure Respiration , Prospective Studies , Respiratory Insufficiency/physiopathology , Respiratory Mechanics , Russia/epidemiology , SARS-CoV-2 , Survival Analysis , Tidal Volume , Treatment Failure
16.
J Med Virol ; 94(4): 1540-1549, 2022 04.
Article in English | MEDLINE | ID: covidwho-1718400

ABSTRACT

Coronavirus disease 2019 (COVID-19) infection in elderly patients is more aggressive and treatments have shown limited efficacy. Our objective is to describe the clinical course and to analyze the prognostic factors associated with a higher risk of mortality of a cohort of patients older than 80 years. In addition, we assess the efficacy of immunosuppressive treatments in this population. We analyzed the data from 163 patients older than 80 years admitted to our institution for COVID-19, during March and April 2020. A Lasso regression model and subsequent multivariate Cox regression were performed to select variables predictive of death. We evaluated the efficacy of immunomodulatory therapy in three cohorts using adjusted survival analysis. The mortality rate was 43%. The mean age was 85.2 years. The disease was considered severe in 76.1% of the cases. Lasso regression and multivariate Cox regression indicated that factors correlated with hospital mortality were: age (hazard ratio [HR] 1.12, 95% confidence interval [CI]: 1.03-1.22), alcohol consumption (HR 3.15, 95% CI: 1.27-7.84), CRP > 10 mg/dL (HR 2.67, 95% CI: 1.36-5.24), and oxygen support with Venturi Mask (HR 6.37, 95% CI: 2.18-18.62) or reservoir (HR 7.87, 95% CI: 3.37-18.38). Previous treatment with antiplatelets was the only protective factor (HR 0.47, 95% CI: 0.23-0.96). In the adjusted treatment efficacy analysis, we found benefit in the combined use of tocilizumab (TCZ) and corticosteroids (CS) (HR 0.09, 95% CI: 0.01-0.74) compared to standard treatment, with no benefit of CS alone (HR 0.95, 95% CI: 0.53-1.71). Hospitalized elderly patients suffer from a severe and often fatal form of COVID-19 disease. In this regard, several parameters might identify high-risk patients upon admission. Combined use of TCZ and CS could improve survival.


Subject(s)
Adrenal Cortex Hormones/administration & dosage , Antibodies, Monoclonal, Humanized/administration & dosage , COVID-19/drug therapy , COVID-19/mortality , Aged, 80 and over , COVID-19/virology , Comorbidity , Drug Therapy, Combination , Female , Hospital Mortality , Hospitalization , Humans , Male , Prognosis , Retrospective Studies , SARS-CoV-2/drug effects , SARS-CoV-2/physiology , Spain/epidemiology , Survival Analysis
17.
JAMA Netw Open ; 5(3): e220773, 2022 03 01.
Article in English | MEDLINE | ID: covidwho-1718200

ABSTRACT

Importance: Women with recent gestational diabetes (GDM) have increased risk of developing type 2 diabetes. Objective: To investigate whether a resource-appropriate and context-appropriate lifestyle intervention could prevent glycemic deterioration among women with recent GDM in South Asia. Design, Setting, and Participants: This randomized, participant-unblinded controlled trial investigated a 12-month lifestyle intervention vs usual care at 19 urban hospitals in India, Sri Lanka, and Bangladesh. Participants included women with recent diagnosis of GDM who did not have type 2 diabetes at an oral glucose tolerance test (OGTT) 3 to 18 months postpartum. They were enrolled from November 2017 to January 2020, and follow-up ended in January 2021. Data were analyzed from April to July 2021. Interventions: A 12-month lifestyle intervention focused on diet and physical activity involving group and individual sessions, as well as remote engagement, adapted to local context and resources. This was compared with usual care. Main Outcomes and Measures: The primary outcome was worsening category of glycemia based on OGTT using American Diabetes Association criteria: (1) normal glucose tolerance to prediabetes (ie, impaired fasting glucose or impaired glucose tolerance) or type 2 diabetes or (2) prediabetes to type 2 diabetes. The primary analysis consisted of a survival analysis of time to change in glycemic status at or prior to the final patient visit, which occurred at varying times after 12 months for each patient. Secondary outcomes included new-onset type 2 diabetes and change in body weight. Results: A total of 1823 women (baseline mean [SD] age, 30.9 [4.9] years and mean [SD] body mass index, 26.6 [4.6]) underwent OGTT at a median (IQR) 6.5 (4.8-8.2) months postpartum. After excluding 160 women (8.8%) with type 2 diabetes, 2 women (0.1%) who met other exclusion criteria, and 49 women (2.7%) who did not consent or were uncontactable, 1612 women were randomized. Subsequently, 11 randomized participants were identified as ineligible and excluded from the primary analysis, leaving 1601 women randomized (800 women randomized to the intervention group and 801 women randomized to usual care). These included 600 women (37.5%) with prediabetes and 1001 women (62.5%) with normoglycemia. Among participants randomized to the intervention, 644 women (80.5%) received all program content, although COVID-19 lockdowns impacted the delivery model (ie, among 644 participants who engaged in all group sessions, 476 women [73.9%] received some or all content through individual engagement, and 315 women [48.9%] received some or all content remotely). After a median (IQR) 14.1 (11.4-20.1) months of follow-up, 1308 participants (81.2%) had primary outcome data. The intervention, compared with usual care, did not reduce worsening glycemic status (204 women [25.5%] vs 217 women [27.1%]; hazard ratio, 0.92; [95% CI, 0.76-1.12]; P = .42) or improve any secondary outcome. Conclusions and Relevance: This study found that a large proportion of women in South Asian urban settings developed dysglycemia soon after a GDM-affected pregnancy and that a lifestyle intervention, modified owing to the COVID-19 pandemic, did not prevent subsequent glycemic deterioration. These findings suggest that alternate or additional approaches are needed, especially among high-risk individuals. Trial Registration: Clinical Trials Registry of India Identifier: CTRI/2017/06/008744; Sri Lanka Clinical Trials Registry Identifier: SLCTR/2017/001; and ClinicalTrials.gov Identifier: NCT03305939.


Subject(s)
Diabetes Mellitus, Type 2/prevention & control , Diabetes, Gestational/prevention & control , Diet , Exercise , Glycemic Control/methods , Life Style , Postpartum Period , Adult , Bangladesh , Blood Glucose , Diabetes Mellitus, Type 2/ethnology , Diabetes, Gestational/ethnology , Female , Glucose Tolerance Test , Humans , India , Pregnancy , Sri Lanka , Survival Analysis , Treatment Outcome , Urban Population
19.
Front Immunol ; 13: 790334, 2022.
Article in English | MEDLINE | ID: covidwho-1715001

ABSTRACT

The capacity of pre-existing immunity to human common coronaviruses (HCoV) to cross-protect against de novo COVID-19is yet unknown. In this work, we studied the sera of 175 COVID-19 patients, 76 healthy donors and 3 intravenous immunoglobulins (IVIG) batches. We found that most COVID-19 patients developed anti-SARS-CoV-2 IgG antibodies before IgM. Moreover, the capacity of their IgGs to react to beta-HCoV, was present in the early sera of most patients before the appearance of anti-SARS-CoV-2 IgG. This implied that a recall-type antibody response was generated. In comparison, the patients that mounted an anti-SARS-COV2 IgM response, prior to IgG responses had lower titres of anti-beta-HCoV IgG antibodies. This indicated that pre-existing immunity to beta-HCoV was conducive to the generation of memory type responses to SARS-COV-2. Finally, we also found that pre-COVID-19-era sera and IVIG cross-reacted with SARS-CoV-2 antigens without neutralising SARS-CoV-2 infectivity in vitro. Put together, these results indicate that whilst pre-existing immunity to HCoV is responsible for recall-type IgG responses to SARS-CoV-2, it does not lead to cross-protection against COVID-19.


Subject(s)
Betacoronavirus/physiology , COVID-19/immunology , Common Cold/immunology , Immunoglobulins, Intravenous/therapeutic use , SARS-CoV-2/physiology , Aged , Aged, 80 and over , Antibodies, Neutralizing/metabolism , Antibodies, Viral/metabolism , Antigens, Viral/immunology , COVID-19/mortality , COVID-19/therapy , Cross Reactions , Female , Humans , Immunity, Heterologous , Immunoglobulin G/metabolism , Immunoglobulin M/metabolism , Immunologic Memory , Male , Middle Aged , Survival Analysis
20.
PLoS One ; 17(2): e0264009, 2022.
Article in English | MEDLINE | ID: covidwho-1703850

ABSTRACT

BACKGROUND: Populations seem to respond differently to the global pandemic of severe acute respiratory syndrome coronavirus 2. Recent studies show individual variability in both susceptibility and clinical response to COVID-19 infection. People with chronic obstructive pulmonary disease (COPD) constitute one of COVID-19 risk groups, being already associated with a poor prognosis upon infection. This study aims contributing to unveil the underlying reasons for such prognosis in people with COPD and the variability in the response observed across worldwide populations, by looking at the genetic background as a possible answer to COVID-19 infection response heterogeneity. METHODS: SNPs already associated with susceptibility to COVID-19 infection (rs286914 and rs12329760) and severe COVID-19 with respiratory failure (rs657152 and rs11385942) were assessed and their allelic frequencies used to calculate the probability of having multiple risk alleles. This was performed on a Portuguese case-control COPD cohort, previously clinically characterized and genotyped from saliva samples, and also on worldwide populations (European, Spanish, Italian, African, American and Asian), using publicly available frequencies data. A polygenic risk analysis was also conducted on the Portuguese COPD cohort for the two mentioned phenotypes, and also for hospitalization and survival to COVID-19 infection. FINDINGS: No differences in genetic risk for COVID-19 susceptibility, hospitalization, severity or survival were found between people with COPD and the control group (all p-values > 0.01), either considering risk alleles individually, allelic combinations or polygenic risk scores. All populations, even those with European ancestry (Portuguese, Spanish and Italian), showed significant differences from the European population in genetic risk for both COVID-19 susceptibility and severity (all p-values < 0.0001). CONCLUSION: Our results indicate a low genetic contribution for COVID-19 infection predisposition or worse outcomes observed in people with COPD. Also, our study unveiled a high genetic heterogeneity across major world populations for the same alleles, even within European sub-populations, demonstrating the need to build a higher resolution European genetic map, so that differences in the distribution of relevant alleles can be easily accessed and used to better manage diseases, ultimately, safeguarding populations with higher genetic predisposition to such diseases.


Subject(s)
COVID-19/genetics , Pulmonary Disease, Chronic Obstructive/diagnosis , Aged , Alleles , COVID-19/complications , COVID-19/pathology , COVID-19/virology , Female , Gene Frequency , Genetic Predisposition to Disease , Genotype , Humans , Male , Middle Aged , Phenotype , Polymorphism, Single Nucleotide , Portugal , Pulmonary Disease, Chronic Obstructive/complications , Respiratory Insufficiency/etiology , Risk Factors , SARS-CoV-2/isolation & purification , Severity of Illness Index , Survival Analysis , /genetics
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